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SBmodel.py
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SBmodel.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/8/20 23:11
# @Author : Daishijun
# @Site :
# @File : SBmodel.py
# @Software : PyCharm
import pandas as pd
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from sklearn.metrics import accuracy_score, log_loss, roc_auc_score
from sklearn.model_selection import train_test_split
from deepctr.models import xDeepFM, DeepFM, DCN
from deepctr.inputs import SparseFeat, DenseFeat, get_fixlen_feature_names
from tensorflow.python.keras.optimizers import Adam
from deepctr.inputs import input_from_feature_columns, get_linear_logit,build_input_features,combined_dnn_input
from deepctr.layers.core import PredictionLayer, DNN
from deepctr.layers.interaction import FM
from deepctr.layers.utils import concat_fun
from tensorflow.python.keras.callbacks import EarlyStopping
from tensorflow.python.keras.utils import multi_gpu_model
import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
data = pd.read_csv('./data/final_track2_train.txt', sep='\t', names=[
'uid', 'user_city', 'item_id', 'author_id', 'item_city', 'channel', 'finish', 'like', 'music_id', 'device', 'time', 'duration_time'])
sparse_features = ['uid', 'user_city', 'item_id', 'author_id', 'item_city', 'channel', 'music_id', 'device']
dense_features = ['time', 'duration_time']
data[sparse_features] = data[sparse_features].fillna('-1', )
data[dense_features] = data[dense_features].fillna(0,)
# target = ['finish', 'like']
target = ['finish', 'like']
for feat in sparse_features:
lbe = LabelEncoder()
data[feat] = lbe.fit_transform(data[feat])
mms = MinMaxScaler(feature_range=(0, 1))
data[dense_features] = mms.fit_transform(data[dense_features])
sparse_feature_columns = [SparseFeat(feat, data[feat].nunique()) #(特征名, 特征不同取值个数)生成SparseFeat对象，name == 特征名，dimension==该特征不同取值个数， dtype ==int32
for feat in sparse_features]
dense_feature_columns = [DenseFeat(feat, 1) #（特征名， dimension==1） 数据dtype == float32
for feat in dense_features]
dnn_feature_columns = sparse_feature_columns + dense_feature_columns
linear_feature_columns = sparse_feature_columns + dense_feature_columns
##['feature1','feature2',...]
feature_names = get_fixlen_feature_names(linear_feature_columns + dnn_feature_columns)
train, test = train_test_split(data, test_size=0.1)
train_model_input = [train[name] for name in feature_names]
test_model_input = [test[name] for name in feature_names]
features = build_input_features(linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
embedding_size=8,
l2_reg=0.00001, init_std=0.0001,
seed=1024)
dnn_input = combined_dnn_input(sparse_embedding_list,dense_value_list)
hidden_1 = tf.keras.layers.Dense(128, activation='relu')(dnn_input)
hidden_1_2 = tf.keras.layers.Dense(128, activation='relu')(hidden_1)
#SB model
hidden_2_finish = tf.keras.layers.Dense(128, activation='relu')(hidden_1_2)
# hidden_2_finish = tf.keras.layers.Dense(128, activation='relu')(hidden_2_finish_1)
hidden_2_like = tf.keras.layers.Dense(128, activation='relu')(hidden_1_2)
# hidden_2_like = tf.keras.layers.Dense(128, activation='relu')(hidden_2_like_1)
dnn_out_finish = tf.keras.layers.Dense(1, activation='sigmoid', name='finish_output')(hidden_2_finish)
dnn_out_like = tf.keras.layers.Dense(1, activation='sigmoid', name='like_output')(hidden_2_like)
# dnn_out_logit = tf.keras.layers.Dense(1, activation=None)(hidden_2)
# dnn_out = tf.keras.layers.Dense(1, activation='sigmoid')(dnn_out_logit)
model = tf.keras.Model(inputs=inputs_list, outputs=[dnn_out_finish, dnn_out_like])
# model = tf.keras.Model(inputs=inputs_list, outputs=[dnn_out_finish])
try:
model = multi_gpu_model(model, gpus=2)
print("Training using multiple GPUs..")
except Exception as e:
print(e)
print("Training using single GPU or CPU..")
model.compile(optimizer=Adam(0.0001), loss={'finish_output':'binary_crossentropy','like_output':'binary_crossentropy'},\
metrics=['accuracy', 'binary_crossentropy'], loss_weights={'finish_output':0.6, 'like_output':0.4})
# model.compile(optimizer=Adam(0.005), loss={'finish_output':'binary_crossentropy'},\
# metrics=['accuracy', 'binary_crossentropy'])
model.summary()
print('train target shape',train[target].values.shape)
history = model.fit(x=train_model_input, y={'finish_output':train['finish'].values, 'like_output':train['like'].values},
validation_split=0.3,callbacks=[EarlyStopping(monitor='val_loss', patience=1, verbose=0, mode='auto')],batch_size=4096, epochs=10, verbose=1)
# history = model.fit(x=train_model_input, y={'finish_output':train['like'].values},
# validation_split=0.3,callbacks=[EarlyStopping(monitor='val_loss', patience=1, verbose=0, mode='auto')],batch_size=4096, epochs=1, verbose=1)
pred_ans_finish, pred_ans_like = model.predict(test_model_input, batch_size=2**14)
# pred_ans_finish = model.predict(test_model_input, batch_size=2**14)
pred_finish = (pred_ans_finish*2).astype(int)
pred_like = (pred_ans_like*2).astype(int)
# print("test accuracy", round(accuracy_score(test[target].values, pred_finish), 4))
# print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4))
# print("test AUC", round(roc_auc_score(test[target].values, pred_ans), 4))
print('test accuracy ==> finish:{} \t like:{}'.format(round(accuracy_score(test['finish'].values, pred_finish), 4),\
round(accuracy_score(test['like'].values, pred_like), 4)))
print('test LogLoss ==> finish:{} \t like:{}'.format(round(log_loss(test['finish'].values, pred_ans_finish), 4),\
round(log_loss(test['like'].values, pred_ans_like), 4)))
print('test AUC ==> finish:{} \t like:{}'.format(round(roc_auc_score(test['finish'].values, pred_ans_finish), 4),\
round(roc_auc_score(test['like'].values, pred_ans_like), 4)))
# print('test accuracy ==> like:{} '.format(round(accuracy_score(test['like'].values, pred_finish), 4)
# ))
# print('test LogLoss ==> like:{} '.format(round(log_loss(test['like'].values, pred_finish), 4)
# ))
# print('test AUC ==> like:{} '.format(round(roc_auc_score(test['like'].values, pred_finish), 4)
# ))